4 citations · 16 across the 12 of their papers we have counts for
13 papers
QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMs
Mohammad Shahverdikondori, Ehsan Mokhtarian, Negar Kiyavash
Causal discovery is essential for understanding relationships among variables of interest in many scientific domains. In this paper, we focus on permutation-based methods for learn…
Causal Effect Identification in a Sub-Population with Latent Variables
Amir Mohammad Abouei, Ehsan Mokhtarian, Negar Kiyavash +1
The s-ID problem seeks to compute a causal effect in a specific sub-population from the observational data pertaining to the same sub population (Abouei et al., 2023). This problem…
Recursive Causal Discovery
Ehsan Mokhtarian, Sepehr Elahi, Sina Akbari +1
Causal discovery, i.e., learning the causal graph from data, is often the first step toward the identification and estimation of causal effects, a key requirement in numerous scien…
CausalCite: A Causal Formulation of Paper Citations
Ishan Kumar, Zhijing Jin, Ehsan Mokhtarian +4
Citation count of a paper is a commonly used proxy for evaluating the significance of a paper in the scientific community. Yet citation measures are widely criticized for failing t…
s-ID: Causal Effect Identification in a Sub-Population
Amir Mohammad Abouei, Ehsan Mokhtarian, Negar Kiyavash
Causal inference in a sub-population involves identifying the causal effect of an intervention on a specific subgroup, which is distinguished from the whole population through the…
Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables
Ehsan Mokhtarian, Mohammadsadegh Khorasani, Jalal Etesami +1
We propose ordering-based approaches for learning the maximal ancestral graph (MAG) of a structural equation model (SEM) up to its Markov equivalence class (MEC) in the presence of…
Causal Effect Identification in Uncertain Causal Networks
Sina Akbari, Fateme Jamshidi, Ehsan Mokhtarian +3
Causal identification is at the core of the causal inference literature, where complete algorithms have been proposed to identify causal queries of interest. The validity of these…
Revisiting the General Identifiability Problem
Yaroslav Kivva, Ehsan Mokhtarian, Jalal Etesami +1
We revisit the problem of general identifiability originally introduced in [Lee et al., 2019] for causal inference and note that it is necessary to add positivity assumption of obs…
A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models
Ehsan Mokhtarian, Saber Salehkaleybar, AmirEmad Ghassami +1
We study experiment design for unique identification of the causal graph of a simple SCM, where the graph may contain cycles. The presence of cycles in the structure introduces maj…
Learning Bayesian Networks in the Presence of Structural Side Information
Ehsan Mokhtarian, Sina Akbari, Fateme Jamshidi +2
We study the problem of learning a Bayesian network (BN) of a set of variables when structural side information about the system is available. It is well known that learning the st…
Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias
Sina Akbari, Ehsan Mokhtarian, AmirEmad Ghassami +1
We consider the problem of learning the causal MAG of a system from observational data in the presence of latent variables and selection bias. Constraint-based methods are one of t…
Causal Effect Identification with Context-specific Independence Relations of Control Variables
Ehsan Mokhtarian, Fateme Jamshidi, Jalal Etesami +1
We study the problem of causal effect identification from observational distribution given the causal graph and some context-specific independence (CSI) relations. It was recently…
A Recursive Markov Boundary-Based Approach to Causal Structure Learning
Ehsan Mokhtarian, Sina Akbari, AmirEmad Ghassami +1
Constraint-based methods are one of the main approaches for causal structure learning that are particularly valued as they are asymptotically guaranteed to find a structure that is…